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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao

    Figure Supplementary S1, Krutilina et al.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2022
    Data sources: Datacite
    ZENODO
    Dataset . 2022
    Data sources: ZENODO
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2022
      Data sources: Datacite
      ZENODO
      Dataset . 2022
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Vaidehi Krishnan; Florian Schmidt; Zahid Nawaz; Prasanna Nori Venkatesh; +14 Authors

    This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation. A summary of the content is provided in the following. R scripts Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R Script to run inferCNV analysis: inferCNV_Zenodo.R Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R RDS files General scRNA-seq Seurat objects: scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS SCENIC files: Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds AUC values computed for regulons: 3.4_regulonAUC.Rds MetaData used in SCENIC cellInfo.Rds Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS BCR-ABL1 inference: HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS NK sub-clustering and filtering: NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS txt and csv files: Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt GSEA results: HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt NK: NK_For_Plotting.txt TFRC and HLA expression: TFRC_and_HLA_Values.txt NATMI result files: UP-regulated_mean.csv DOWN-regulated_mean.csv Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv Compressed folders: All CyTOF raw data files: CyTOF_Data_raw.zip Results of the patient-based classifier: PatientwiseClassifier.zip Results of the single-cell based classifier: SingleCellClassifierResults.zip For general new data analysis approaches, we recommend the readers to use the Seruat object stored in DUKE_final_for_Shiny_App.rds or to use the shiny app(http://scdbm.ddnetbio.com/) and perform further analysis from there. RAW data is available at EGA upon request using Study ID: EGAS00001005509 Revision The for_CML_manuscript_revision.tar.gz folder contains scripts and data for the paper revision including 1) Detection of the BCR-ABL fusion with long read sequencing; 2) Identification of BCR-ABL junction reads with scRNAseq; 3) Detection of expressed mutations using scRNAseq.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2021
    Data sources: Datacite
    ZENODO
    Dataset . 2021
    Data sources: ZENODO
    ZENODO
    Dataset . 2021
    Data sources: ZENODO
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2021
      Data sources: Datacite
      ZENODO
      Dataset . 2021
      Data sources: ZENODO
      ZENODO
      Dataset . 2021
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Pérez Brunius, P; García Carrillo, P; Ronquillo Méndez, A; Rodríguez Outerelo, J; +3 Authors

    This dataset is comprised of the final processed data collected by 207 surface drifters deployed in the western Gulf of Mexico during the Deep Water Dispersion Experiment (DWDE), as part of the project “Implementación de redes de observación oceanográficas (físicas geoquímicas, ecológicas) para la generación de escenarios ante posibles contingencias relacionadas a la exploración y producción de hidrocarburos en aguas profundas del Golfo de México” of the Gulf de Mexico Research Consortium (CIGoM), funded by CONACYT‐SENER‐Hydrocarbon Fund, Mexico”. The DWDE drifter data is also made available with the GulfDriftersWithDWDE dataset (http://doi.org/10.5281/zenodo.3985916), which is a merged and uniformly processed surface drifter dataset which includes publicly available data from several different drifter experiments. These latter database is the basis for the gridded product GulfFlow (http://doi.org/10.5281/zenodo.3978793). See Lilly and Pérez-Brunius (2021) "A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements" for details. García Carrillo, P., Ronquillo Méndez, A., Rodríguez Outerelo, J., Pérez Brunius, P. 2018. Experimento de Dispersión en Aguas Profundas ( DWDE ). Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California. 253 pp. Pérez-Brunius P., P. García Carrillo, A. Ronquillo Méndez, J. Rodríguez Outerelo, A. Sandoval Rangel, C. Liera Grijalva, X. Flores Vidal (2019). Trayectorias de Derivadores Superficiales en el Oeste del Golfo de México del Experimento de Dispersión en Aguas Profundas. Reg. INDAUTOR, México (03-2019- 120510255600-01, 13 December 2019). The acquisition of the surface drifter data from the DWDE experiment has been financed by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico (project 201441), developed by the Gulf of Mexico Research Consortium (CIGoM). We recognize PEMEX for promoting to the Fund the specific demand on oil spills and the environment. See database webpage with additional information (https://giola.cicese.mx/database/DWDE) {"references": ["Garc\u00eda Carrillo, P., Ronquillo M\u00e9ndez, A., Rodr\u00edguez Outerelo, J., P\u00e9rez Brunius, P. 2018. Experimento de Dispersi\u00f3n en Aguas Profundas ( DWDE ). Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, Baja California. 253 pp."]}

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2021
    Data sources: Datacite
    ZENODO
    Dataset . 2021
    Data sources: ZENODO
    ZENODO
    Dataset . 2020
    Data sources: ZENODO
    ZENODO
    Dataset . 2020
    Data sources: ZENODO
    ZENODO
    Dataset . 2020
    Data sources: ZENODO
    ZENODO
    Dataset . 2021
    Data sources: ZENODO
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2021
      Data sources: Datacite
      ZENODO
      Dataset . 2021
      Data sources: ZENODO
      ZENODO
      Dataset . 2020
      Data sources: ZENODO
      ZENODO
      Dataset . 2020
      Data sources: ZENODO
      ZENODO
      Dataset . 2020
      Data sources: ZENODO
      ZENODO
      Dataset . 2021
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao

    EmERGE - WP3 Lisbon data

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2020
    Data sources: Datacite
    ZENODO
    Dataset . 2020
    Data sources: ZENODO
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2020
      Data sources: Datacite
      ZENODO
      Dataset . 2020
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Guleria, Abhay; Sumedha, Chakma;

    This database contains an "excel sheet" and ".docx" files having input parameters and health risk metrics generated from a Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment Framework for Ariyamangalam dumping site.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2021
    Data sources: Datacite
    ZENODO
    Dataset . 2021
    Data sources: ZENODO
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2021
      Data sources: Datacite
      ZENODO
      Dataset . 2021
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Zioulis, Nikolaos; Karakottas, Antonis; Zarpalas, Dimitrios; Daras, Petros;

    Modern 3D vision advancements rely on data driven methods and thus, task specific annotated datasets. Especially for geometric inference tasks like depth and surface estimation, the collection of high quality data is very challenging, expensive and laborious. While considerable efforts have been made for traditional pinhole cameras, the same cannot be said for omnidirectional ones. 3D60 is a collective dataset generated in the context of various 360o vision research works. It comprises multi-modal omnidirectional stereo renders of scenes from realistic and synthetic large-scale 3D datasets (Matterport3D, Stanford2D3D and SunCG). Our dataset fills a very important gap in data-driven spherical 3D vision and, more specifically, for the monocular and stereo dense depth and surface estimation tasks. We originate by exploiting the efforts made in providing synthetic and real scanned 3D datasets of interior spaces and re-using them via ray-tracing in order to generate high quality, densely annotated spherical panoramas. Instructions, code and data splits available @ https://vcl3d.github.io/3D60/

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2019
    Data sources: Datacite
    ZENODO
    Dataset . 2019
    Data sources: ZENODO
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2019
      Data sources: Datacite
      ZENODO
      Dataset . 2019
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Francesco Sturla; Alessandro Caimi; Rodrigo M Romarowski; Giovanni Nano; +3 Authors

    Sturla F, Caimi A, Romarowski RM, Nano G, Glauber M, Redaelli A, Votta E, Marrocco-Trischitta MM. Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant. J Endovasc Ther. 2022 May 19:15266028221095403. doi: 10.1177/15266028221095403. Epub ahead of print. PMID: 35588222. Abstract Purpose: Displacement forces (DFs) identify hostile landing zones for stent graft deployment in thoracic endovascular aortic repair (TEVAR). However, their use in TEVAR planning is hampered by the need for time-expensive computational fluid dynamics (CFD). We propose a novel fast-approximate computation of DFs merely exploiting aortic arch anatomy, as derived from the computed tomography (CT) and a measure of central aortic pressure. Materials and methods: We tested the fast-approximate approach against CFD gold-standard in 34 subjects with the "bovine" aortic arch variant. For each dataset, a 3-dimensional (3D) model of the aortic arch lumen was reconstructed from computed tomography angiography and CFD then employed to compute DFs within the aortic proximal landing zones. To quantify fast-approximate DFs, the wall shear stress contribution to the DF was neglected and blood pressure space-distribution was averaged on the entire aortic wall to reliably approximate the patient-specific central blood pressure. Also, DF values were normalized on the corresponding proximal landing zone area to obtain the equivalent surface traction (EST). Results: Fast-approximate approach consistently reflected (r2=0.99, p<0.0001) the DF pattern obtained by CFD, with a -1.1% and 0.7° bias in DFs magnitude and orientation, respectively. The normalized EST progressively increased (p<0.0001) from zone 0 to zone 3 regardless of the type of arch, with proximal landing zone 3 showing significantly greater forces than zone 2 (p<0.0001). Upon DF normalization to the corresponding aortic surface, fast-approximate EST was decoupled in blood pressure and a dimensionless shape vector (S) reflecting aortic arch morphology. S showed a zone-specific pattern of orientation and proved a valid biomechanical blueprint of DF impact on the thoracic aortic wall. Conclusion: Requiring only a few seconds and quantifying clinically relevant biomechanical parameters of proximal landing zones for arch TEVAR, our method suits the real preoperative decision-making process. It paves the way toward analyzing large population of patients and hence to define threshold values for a future patient-specific preoperative TEVAR planning.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2023
    Data sources: Datacite
    ZENODO
    Dataset . 2023
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      Data sources: Datacite
      ZENODO
      Dataset . 2023
      Data sources: ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Haug, Benedikt; Messmer, Monika; Goldringer, Isabelle; Enjalbert, Jérôme; +1 Authors

    This dataset contains agronomic measurement data of pure stands and mixed stands of 27 pea genotypes (P. sativum L.) and 8 barley genotypes (H. vulgare L.), sown in an incomplete factorial (Haug et al., 2021) at two locations in Switzerland over two years (2018 and 2019) under organic management. It contains 13'522 data points collected on 960 micro plots. total yield mixed stand/pure stand yield fraction yields of pea and barley in mixed stand land equivalent ratios (LERs) early vigor early canopy height (1st measurement) early canopy height (2nd measurement) canopy height at grain filling canopy height at maturity onset of pea flowering total biomass stipule length stipule size stipule area lodging at grain filling lodging at maturity The data set contains data used in the PhD thesis of Benedikt Haug (http://theses.fr/2021UPASB042). The data was acquired in the framework of the ReMIX project (https://www.remix-intercrops.eu/) which was funded by the European Union's Horizon 2020 research and innovation program, under grant agreement nr. 727217. Literature: Haug, B., Messmer, M. M., Enjalbert, J., Goldringer, I., Forst, E., Flutre, T., et al. (2021). Advances in Breeding for Mixed Cropping – Incomplete Factorials and the Producer/Associate Concept. Front. Plant Sci. 11. doi: 10.3389/fpls.2020.620400.

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    Dataset . 2022
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      ZENODO
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      ZENODO
      Dataset . 2022
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    Authors: Spyridon N. Papageorgiou; Raphael Tilen; Vaska Vandevska-Radunovic; Theodore Eliades;

    Dataset for all analyses

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    Dataset . 2020
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      ZENODO
      Dataset . 2020
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      Dataset . 2020
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Carlos Paradis; Rick Kazman; Mike Konrad; Robert Stoddard;

    This data package contains supplemental material data for the under review TSE submission: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis. The restricted access requirement will be lifted upon approval of the manuscript. The comprehensive explanation of this dataset can be found at: https://sailuh.github.io/causal_commit_flow_docs The following briefly describes the contents of the folders. The analysis presented in the manuscript requires the following: Git Log Mailing List Software Vulnerabilities (NVD Feed) This data is provided to a mining software repository tool, Kaiaulu. The data specifications and configuration parameters are defined in the OpenSSL project configuration file (.yml), also included in this package. An R notebook in Kaiaulu, taking the dataset above + project configuration file, can then perform the first analysis step: https://github.com/sailuh/kaiaulu/blob/master/vignettes/issue_social_smell_showcase.Rmd The file 1_openssl_social_smells_timeline.csv is generated as an output of this R Notebook, and included in the causal_model folder of this package. The following files in this folder numbered 2 through 16, describe transformation steps using Excel, Python scripts, and Tetrad (also an open source tool). These are described conceptually in the manuscript, but in more detail in the comprehensive explanation of this dataset linked at the start.

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    ZENODO
    Dataset . 2023
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    Dataset . 2023
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      ZENODO
      Dataset . 2023
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      ZENODO
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao

    Figure Supplementary S1, Krutilina et al.

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    ZENODO
    Dataset . 2022
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      Data sources: Datacite
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      Dataset . 2022
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Vaidehi Krishnan; Florian Schmidt; Zahid Nawaz; Prasanna Nori Venkatesh; +14 Authors

    This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation. A summary of the content is provided in the following. R scripts Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R Script to run inferCNV analysis: inferCNV_Zenodo.R Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R RDS files General scRNA-seq Seurat objects: scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS SCENIC files: Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds AUC values computed for regulons: 3.4_regulonAUC.Rds MetaData used in SCENIC cellInfo.Rds Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS BCR-ABL1 inference: HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS NK sub-clustering and filtering: NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS txt and csv files: Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt GSEA results: HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt NK: NK_For_Plotting.txt TFRC and HLA expression: TFRC_and_HLA_Values.txt NATMI result files: UP-regulated_mean.csv DOWN-regulated_mean.csv Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv Compressed folders: All CyTOF raw data files: CyTOF_Data_raw.zip Results of the patient-based classifier: PatientwiseClassifier.zip Results of the single-cell based classifier: SingleCellClassifierResults.zip For general new data analysis approaches, we recommend the readers to use the Seruat object stored in DUKE_final_for_Shiny_App.rds or to use the shiny app(http://scdbm.ddnetbio.com/) and perform further analysis from there. RAW data is available at EGA upon request using Study ID: EGAS00001005509 Revision The for_CML_manuscript_revision.tar.gz folder contains scripts and data for the paper revision including 1) Detection of the BCR-ABL fusion with long read sequencing; 2) Identification of BCR-ABL junction reads with scRNAseq; 3) Detection of expressed mutations using scRNAseq.

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    ZENODO
    Dataset . 2021
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    Dataset . 2021
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    Dataset . 2021
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      Dataset . 2021
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    Authors: Pérez Brunius, P; García Carrillo, P; Ronquillo Méndez, A; Rodríguez Outerelo, J; +3 Authors

    This dataset is comprised of the final processed data collected by 207 surface drifters deployed in the western Gulf of Mexico during the Deep Water Dispersion Experiment (DWDE), as part of the project “Implementación de redes de observación oceanográficas (físicas geoquímicas, ecológicas) para la generación de escenarios ante posibles contingencias relacionadas a la exploración y producción de hidrocarburos en aguas profundas del Golfo de México” of the Gulf de Mexico Research Consortium (CIGoM), funded by CONACYT‐SENER‐Hydrocarbon Fund, Mexico”. The DWDE drifter data is also made available with the GulfDriftersWithDWDE dataset (http://doi.org/10.5281/zenodo.3985916), which is a merged and uniformly processed surface drifter dataset which includes publicly available data from several different drifter experiments. These latter database is the basis for the gridded product GulfFlow (http://doi.org/10.5281/zenodo.3978793). See Lilly and Pérez-Brunius (2021) "A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements" for details. García Carrillo, P., Ronquillo Méndez, A., Rodríguez Outerelo, J., Pérez Brunius, P. 2018. Experimento de Dispersión en Aguas Profundas ( DWDE ). Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California. 253 pp. Pérez-Brunius P., P. García Carrillo, A. Ronquillo Méndez, J. Rodríguez Outerelo, A. Sandoval Rangel, C. Liera Grijalva, X. Flores Vidal (2019). Trayectorias de Derivadores Superficiales en el Oeste del Golfo de México del Experimento de Dispersión en Aguas Profundas. Reg. INDAUTOR, México (03-2019- 120510255600-01, 13 December 2019). The acquisition of the surface drifter data from the DWDE experiment has been financed by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico (project 201441), developed by the Gulf of Mexico Research Consortium (CIGoM). We recognize PEMEX for promoting to the Fund the specific demand on oil spills and the environment. See database webpage with additional information (https://giola.cicese.mx/database/DWDE) {"references": ["Garc\u00eda Carrillo, P., Ronquillo M\u00e9ndez, A., Rodr\u00edguez Outerelo, J., P\u00e9rez Brunius, P. 2018. Experimento de Dispersi\u00f3n en Aguas Profundas ( DWDE ). Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, Baja California. 253 pp."]}

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    ZENODO
    Dataset . 2021
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    Dataset . 2021
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    Dataset . 2020
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    ZENODO
    Dataset . 2020
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    Dataset . 2020
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    Dataset . 2021
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